from functools import partial
import torch
import torch.nn as nn


class PatchEmbed(nn.Module):

    def __init__(self, img_size, patch_size, in_c, embed_dim, norm_layer=None):
        super().__init__()
        img_size = (img_size, img_size)
        patch_size = (patch_size, patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0],
                          img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]

        self.proj = nn.Conv2d(in_c,
                              embed_dim,
                              kernel_size=patch_size,
                              stride=patch_size)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1]

        # flatten: [B, C, H, W] -> [B, C, HW]
        # transpose: [B, C, HW] -> [B, HW, C]
        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        return x


class Attention(nn.Module):

    def __init__(self, embed_dim, num_heads, attn_drop_ratio, proj_drop_ratio):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        head_dim = embed_dim // num_heads
        self.scale = head_dim**-0.5
        self.qkv = nn.Linear(embed_dim, embed_dim * 3, bias=True)
        self.attn_drop = nn.Dropout(attn_drop_ratio)
        self.proj = nn.Linear(embed_dim, embed_dim)
        self.proj_drop = nn.Dropout(proj_drop_ratio)

    def forward(self, x):
        # [batch_size, num_patches+1, total_embed_dim]
        B, N, C = x.shape

        # qkv(): -> [batch_size, num_patches+1, 3*total_embed_dim]
        # reshape: -> [batch_size, num_patches+1, 3, num_heads, embed_dim_per_head]
        # permute: -> [3, batch_size, num_heads, num_patches+1, embed_dim_per_head]
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
        # [batch_size, num_heads, num_patches+1, embed_dim_per_head]
        q, k, v = qkv[0], qkv[1], qkv[2]

        # transpose: -> [batch_size, num_heads, embed_dim_per_head, num_patches+1]
        # @: multiply -> [batch_size, num_heads, num_patches+1, num_patches+1]
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        # @: multiply -> [batch_size, num_heads, num_patches+1, embed_dim_per_head]
        # transpose: -> [batch_size, num_patches+1, num_heads, embed_dim_per_head]
        # reshape: -> [batch_size, num_patches+1, total_embed_dim]
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Mlp(nn.Module):

    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Block(nn.Module):

    def __init__(self,
                 embed_dim,
                 num_heads,
                 mlp_ratio,
                 uni_drop_ratio,
                 attn_drop_ratio,
                 blk_drop_ratio,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm):
        super(Block, self).__init__()
        self.norm1 = norm_layer(embed_dim)
        self.attn = Attention(embed_dim=embed_dim,
                              num_heads=num_heads,
                              attn_drop_ratio=attn_drop_ratio,
                              proj_drop_ratio=uni_drop_ratio)
        self.drop = nn.Dropout(
            blk_drop_ratio) if blk_drop_ratio > 0. else nn.Identity()
        self.norm2 = norm_layer(embed_dim)
        mlp_hidden_dim = int(embed_dim * mlp_ratio)
        self.mlp = Mlp(in_features=embed_dim,
                       hidden_features=mlp_hidden_dim,
                       act_layer=act_layer,
                       drop=uni_drop_ratio)

    def forward(self, x):
        x = x + self.drop(self.attn(self.norm1(x)))
        x = x + self.drop(self.mlp(self.norm2(x)))
        return x


class VisionTransformer(nn.Module):

    def __init__(
            self,
            # Initialized at model generation
            img_size,
            patch_size,
            num_classes,
            embed_dim,
            depth,
            num_heads,
            # Need to be initialized in this function
            in_c=3,
            mlp_ratio=4.0,
            uni_drop_ratio=0.,
            attn_drop_ratio=0.,
            blk_drop_ratio=0.,
            embed_layer=PatchEmbed,
            norm_layer=None,
            act_layer=None):
        super(VisionTransformer, self).__init__()
        self.num_classes = num_classes
        self.embed_dim = embed_dim
        self.num_tokens = 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

        self.patch_embed = embed_layer(img_size=img_size,
                                       patch_size=patch_size,
                                       in_c=in_c,
                                       embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(
            torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        self.pos_drop = nn.Dropout(p=uni_drop_ratio)

        # stochastic depth decay rule
        self.blocks = nn.Sequential(*[
            Block(embed_dim=embed_dim,
                  num_heads=num_heads,
                  mlp_ratio=mlp_ratio,
                  uni_drop_ratio=uni_drop_ratio,
                  attn_drop_ratio=attn_drop_ratio,
                  blk_drop_ratio=blk_drop_ratio,
                  norm_layer=norm_layer,
                  act_layer=act_layer) for i in range(depth)
        ])
        self.norm = norm_layer(embed_dim)

        # Classifier head(s)
        self.head = nn.Linear(
            self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        # Weight init
        nn.init.trunc_normal_(self.pos_embed, std=0.02)
        nn.init.trunc_normal_(self.cls_token, std=0.02)
        self.apply(_init_vit_weights)

    def forward_features(self, x):
        # [B, C, H, W] -> [B, num_patches, embed_dim]
        x = self.patch_embed(x)  # [B, 196, 768]
        # [1, 1, 768] -> [B, 1, 768]
        cls_token = self.cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_token, x), dim=1)  # [B, 197, 768]

        x = self.pos_drop(x + self.pos_embed)
        x = self.blocks(x)
        x = self.norm(x)
        return x[:, 0]

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


def _init_vit_weights(m):
    if isinstance(m, nn.Linear):
        nn.init.trunc_normal_(m.weight, std=.01)
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode="fan_out")
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.LayerNorm):
        nn.init.zeros_(m.bias)
        nn.init.ones_(m.weight)


def make_vit(num_classes: int = 1000,
             img_size: int = 224,
             patch_size: int = 16,
             embed_dim: int = 768,
             depth: int = 12,
             num_heads: int = 8):
    model = VisionTransformer(img_size=img_size,
                              patch_size=patch_size,
                              embed_dim=embed_dim,
                              depth=depth,
                              num_heads=num_heads,
                              num_classes=num_classes)
    return model
